The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 02, 2022

Filed:

Jun. 13, 2019
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Ali Kamen, Skillman, NJ (US);

Ahmet Tuysuzoglu, Jersey City, NJ (US);

Bin Lou, Princeton, NJ (US);

Bibo Shi, Monmouth Junction, NJ (US);

Nicolas Von Roden, St Gallen, CH;

Kareem Abdelrahman, Giza, EG;

Berthold Kiefer, Erlangen, DE;

Robert Grimm, Nuremberg, DE;

Heinrich von Busch, Uttenreuth, DE;

Mamadou Diallo, Plainsboro, NJ (US);

Tongbai Meng, Ellicott City, MD (US);

Dorin Comaniciu, Princeton Junction, NJ (US);

David Jean Winkel, Basel, CH;

Xin Yu, Nashville, TN (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06N 20/00 (2019.01); G06K 9/62 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06K 9/6256 (2013.01); G06K 9/6269 (2013.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.


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